# jasontang-ai/Context-Engineering

"Context engineering is the delicate art and science of filling the context window with just the right information for the next step." — Andrej Karpathy. A frontier, first-principles handbook inspired by Karpathy and 3Blue1Brown for moving beyond prompt engineering to the wider discipline of context design, orchestration, and optimization.

Repository: https://github.com/jasontang-ai/Context-Engineering
Canonical: https://ross.abutalabs.com/products/context-engineering
Homepage: https://deepwiki.com/davidkimai/Context-Engineering
Language: Python
License: MIT
License Family: permissive
Last push: 2026-02-27T05:04:18+00:00

## Health v2 (maintenance only)
Score: 49/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 69, release rhythm 35, longevity 30
- inputs: {"age_days": 431, "days_push": 187, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 9228, forks 1029 (observed 2026-08-28T04:10:28.976854+00:00)

## What it is
A first-principles handbook and course on context engineering — designing, orchestrating, and optimizing the full information payload given to LLMs beyond simple prompting. It combines visual explanations, research paper summaries, templates, and agent command integrations for tools like Claude Code.

## Use cases
- learn context engineering for llms
- move beyond prompt engineering to context design
- understand how to fill an llm context window effectively
- study research on context optimization for large language models
- find templates and patterns for building llm agents
- take a structured course on context engineering

## When to choose
- you want a conceptual, research-grounded education in context design rather than a production library
- you are designing prompts, memory systems, or multi-agent context flows and want patterns and templates
- you want curated links to recent context-engineering research papers

## When to avoid
- you need a production-ready SDK or runtime framework with stable APIs
- you want a simple prompt library without theory or coursework
- you need guaranteed stability — the material is explicitly frontier and under active construction

## Facets
- artifact type: learning-resource
- maturity: active
- function: prompt-engineering, rag, agent-framework, documentation
- domain: large-language-models, artificial-intelligence, tutorials
- platform: python, cross-platform
- tags: context-engineering, handbook, prompt-design, llm-context-window, first-principles, course, ai-agents, retrieval-augmented-generation

## Member repositories
- jasontang-ai/Context-Engineering (main) score 49

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:28.976854+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:23:16.004765+00:00, confidence not recorded.
  - readme: https://github.com/jasontang-ai/Context-Engineering (fetched 2026-08-28T04:10:28.976854+00:00, sha 6886b0517948)
  - homepage: https://deepwiki.com/davidkimai/Context-Engineering (fetched 2026-08-29T08:23:06.326854+00:00, sha 4781414ab292)
  - site_page: https://deepwiki.com/davidkimai/Context-Engineering/2-getting-started (fetched 2026-08-29T08:23:06.336655+00:00, sha e669c52ad8a9)
  - site_page: https://deepwiki.com/davidkimai/Context-Engineering/10-reference-documentation (fetched 2026-08-29T08:23:06.339339+00:00, sha 821e2d6d423f)
- Data as of 2026-08-30T08:39:29.467469+00:00.